Abstract
Abstract Automotive industry targets such as complying with emission legislations and increasing fuel economy, require the improvement of air-fuel ratio control systems. Oxygen sensors are a crucial part of these control systems and regulations oblige monitoring of their performance and detecting sensor-related faults. The primary purpose of this paper is to develop a methodology for precise and accurate monitoring and diagnosis of oxygen sensors to meet legislations and performance targets while the required calibration effort is reduced. Input parameters with the highest correlation factors were selected to be utilized in different system identification methodologies to statistically determine the most fitting model. In the end, a NARX model with two hidden layers and eight neurons in each hidden layer with standard deviation and mean threshold values was determined to be the optimum design to detect if the oxygen sensor was functioning or faulty.
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